XML to R DataFrame Converter

Transform XML data into R DataFrame code with automatic type detection

XML Input

R DataFrame Output

About XML to R DataFrame Converter

Convert XML data to R data frame code. Perfect for statistical analysis, data science workflows, and R programming with XML data.

Key Features

  • Automatic Type Detection: Infers column types (logical, integer, numeric, character)
  • data.frame() Generation: Creates ready-to-run R code
  • Column Name Sanitization: Converts XML tags to valid R variable names
  • NA Handling: Properly handles missing values as NA
  • XML Parser: Accurately parses XML with table-like structures
  • Type Comments: Includes helpful comments about data types
  • Copy & Download: Save as .R file for RStudio

How to Use

  1. Input XML Data: Paste your XML data or upload an .xml file
  2. Review Output: The R DataFrame code generates automatically
  3. Copy or Download: Use the Copy or Download button to save your .R file
  4. Run in R: Execute the code in R, RStudio, or Jupyter notebooks

Supported XML Structures

The converter recognizes several common XML table patterns:

  • <table><row>...</row></table>: Standard table structure
  • <data><record>...</record></data>: Data records pattern
  • <records><record>...</record></records>: Records collection
  • Repeated Elements: Any root with repeated child elements
  • Attributes: Also supports attribute-based data

Type Detection

The converter automatically detects column types based on data values:

  • logical: Values matching "TRUE" or "FALSE" (case-insensitive)
  • integer: Whole numbers without decimal points
  • numeric: Numbers with decimal points or scientific notation
  • character: Text values (default type)

Example Conversion

XML Input:

<?xml version="1.0" encoding="UTF-8"?>
<data>
  <record>
    <Name>John Doe</Name>
    <Age>28</Age>
    <City>New York</City>
    <Department>Engineering</Department>
  </record>
  <record>
    <Name>Jane Smith</Name>
    <Age>34</Age>
    <City>London</City>
    <Department>Marketing</Department>
  </record>
</data>

R DataFrame Output:

# Rows: 2, Columns: 4
# Column types: Name (character), Age (integer), City (character), Department (character)

df <- data.frame(
  Name = c("John Doe", "Jane Smith"),
  Age = c(28, 34),
  City = c("New York", "London"),
  Department = c("Engineering", "Marketing"),
  stringsAsFactors = FALSE
)

print(df)

Common Use Cases

  • Statistical Analysis: Import XML data for R statistical analysis
  • Data Science: Use XML data in data science workflows
  • Machine Learning: Prepare XML data for ML models in R
  • Visualization: Create plots and charts with ggplot2
  • Research: Analyze research data from XML sources
  • Reporting: Generate reports with R Markdown

R DataFrame Features

  • stringsAsFactors = FALSE: Prevents automatic factor conversion
  • c() Vectors: Uses R's combine function for column data
  • NA Values: Empty cells converted to NA
  • Type Preservation: Maintains numeric and logical types
  • Print Statement: Includes print(df) for immediate output

Column Name Sanitization

XML tags are automatically converted to valid R variable names:

  • Replaces special characters with underscores
  • Removes leading/trailing underscores
  • Prefixes numbers with 'X' (e.g., "1st" becomes "X1st")
  • Handles duplicate names with numeric suffixes (_2, _3, etc.)
  • Generates default names (column_1, column_2) for empty tags

Using in R

  1. Copy or download the generated R code
  2. Open RStudio or R console
  3. Paste and run the code
  4. The data frame 'df' will be created and displayed
  5. Use df in your analysis, visualizations, or models

R Packages Compatibility

The generated data frames work seamlessly with popular R packages:

  • dplyr: Data manipulation and transformation
  • ggplot2: Data visualization and plotting
  • tidyr: Data tidying and reshaping
  • caret: Machine learning and modeling
  • data.table: High-performance data operations
  • rmarkdown: Dynamic reporting

Tips for Best Results

  • Ensure XML has consistent structure across records
  • Use consistent data types within columns
  • Avoid special characters in tag names when possible
  • Test with sample data before processing large XML files
  • Review column types in comments and adjust if needed
  • Use stringsAsFactors = TRUE if you need factor columns

Privacy & Security

All conversions happen locally in your browser. Your XML data is never uploaded to any server, ensuring complete privacy and security.